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Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [ABSTRACT FROM AUTHOR]

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis. [ABSTRACT FROM AUTHOR]

• Synthetic thermal comfort profiles were generated from ASHRAE thermal comfort databases I and II used to evaluate occupant satisfaction and to calculate the HVAC setpoints. • Corrective Power of PCS was integrated into the Comfort Profiles. • PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18%. • ASHRAE requirement of 80% occupant satisfaction is met with little increase in energy consumption. Personal Comfort Systems (PCS) are energy-efficient devices that create a microenvironment around occupants and can be controlled individually to improve personal comfort. Most studies related to PCS either focus on improving occupants' thermal satisfaction or evaluate the energy-saving potential of PCS devices but do not consider the energy and comfort consequences simultaneously. This paper investigates the impact of PCS on improving occupants' thermal satisfaction and energy consumption across different climates in the U.S. using 16 representative cities. To account for the diversity in individual preferences, 400 comfort profiles for four climatic zones for summer and winter were generated utilizing comfort votes from ASHRAE global thermal comfort database I and II. PCS devices were used to improve occupant satisfaction by integrating the Corrective Power (CP) values into the comfort profiles. CP is defined as the temperature difference between two ambient temperatures that achieve the same thermal sensation one with PCS and another without PCS (reference condition). Then we compute the zone level setpoints that maximize the number of satisfied occupants and evaluate energy consumption of those zone level setpoints using EnergyPlus. Our study includes 3 cases; Case 1: without PCS (baseline), Case 2: only fans in summer and heaters in winter, and Case 3: fans and heaters in both summers and winters as required to improve satisfaction. In each of the three cases, we evaluated the comfort consequences using the comfort profiles, and the energy consumption implications. This accounts for the change in HVAC setpoints and the power requirements of PCS devices. The results show that incorporating PCS can increase occupants' satisfaction from 68% in Case 1 to 77.8% in Case 2 and 86.6% in Case 3. Moreover, energy consumption results in Case 2 are 0–1% higher, and 0–1.3% higher in Case 3 compared to without PCS, depending on the climate. Our results demonstrate that the adoption of PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18% and meet the ASHRAE requirement of 80% occupant satisfaction with little increase in energy consumption. The comfort profiles generated in this study are also released as a dataset to support future studies. [ABSTRACT FROM AUTHOR]

• Two model buildings are specially built for comparative study. • Dynamic monitoring of external insulation for improving thermal environment and energy saving during heating. • The time for EPS building to approach the air temperature set value has no obvious advantage. • The energy saving effect of short-time heating operation of EPS building is very weak. • A dynamic index is proposed to evaluate the improvement effect of external insulation on indoor thermal environment. In this paper, two identical model buildings were built, one of which adopted 38 mm Expanded Polystyrene (EPS) board as the external insulation. Under the same heating set-point temperature, the dynamic change process of indoor thermal environment and heating energy consumption of EPS insulation building and non-insulation Reference building was compared and monitored. Results show that under the same heating set-point temperature, the indoor temperature of the two buildings can quickly approach the set value in about 20 min. However, the temperature rise of the internal surface of the wall lags behind. After several hours, the advantage of the temperature rise of the internal surface of the EPS building gradually appeared. The energy consumption of the two buildings is similar in the initial heating period of 20 min, and the energy saving rate of EPS building increases slowly with the increase of the heating duration. The energy saving rate is about 1%–26% in the range of 0.5–8 h intermittent heating in hot summer and cold winter zone. The change curve of cumulative temperature rise ratio with heating time is proposed to evaluate the dynamic improvement effect of external insulation on indoor thermal environment. This research provides a reference basis for the development of energy conservation. [ABSTRACT FROM AUTHOR]

The objective of this article is to improve diagnosing and data availability for all stakeholders, including building operators, planners, inhabitants and utility suppliers. This paper presents the results concerning the demand for final and primary usable energy. For each of the buildings a preliminary energy audit was prepared and this was the basis for comparison between the estimated energy consumption in the buildings before and after thermal upgrading. The research was carried out mainly in buildings belonging to the Department of Public Utilities and Housing in Zielona Góra. The group of selected buildings consisted mainly of traditional technology buildings having different functions, i.e. residential, residential-commercial, commercial, and other buildings (schools and boarding houses). Forty buildings were selected for analysis. They were built in different years and had different functions, i.e.: 23 residential buildings, 6 residential commercial buildings, 7 commercial buildings, and 4 buildings had other functions (schools). They are the main energy consumers, which has a considerable impact on the environment. [ABSTRACT FROM AUTHOR]

Current indoor thermal environment is usually automatically controlled based on a temperature set-point given by room occupants or building managers. However, investigation shows that many temperature set-points are far from the comfortable temperature range recommended by design handbooks. Such unreasonable temperature set-points will result in not only uncomfortable indoor thermal environment, but also waste of cooling or heating energy. The authors have proposed a methodology of satisfaction based control to take place of the traditional set-point based control. This paper describes the experimental comparison results between satisfaction based indoor thermal environment control and set-point based control. Two test-beds were set up and a series of experiments was conducted to compare control performances, user acceptances, user work performances and system energy consumptions of the set-point based and satisfaction based control. The comparison results show that the satisfaction based control can achieve more stable thermal environment. Both of the two control methods can get relatively high scores regarding user acceptance and work performance. There are no statistically significant differences for user acceptance and work performance between two control methods. However, satisfaction based control consumed 15.3% and 11.9% less energy at two test-beds respectively than set-point based control due to more reasonable temperature settings given by the satisfaction based control. [ABSTRACT FROM AUTHOR]

The effective and efficient utilization of electrical energy is the outcome of smart energy management system. Moreover, appropriate augmentation of renewable energy alternatives through distributed generation can considerably unburden the power utility system. This paper identifies the scope for the rule based approach to develop an energy efficient utilization model for bulk power consumers. The augmentation of distributed power generation using solar PV has also been proposed which considerably reduces the maximum demand (M.D) of power during peak hours. The distributed generation from solar PV for this case-study uses minimum storage battery as the generation schedule and the load schedule often coincides. The portfolio of energy distribution is formulated based on priority of energy demand. The proposed model can be suitably modified for similar type of load centers including industrial loads. [ABSTRACT FROM AUTHOR]

Lack of information on energy consumption patterns of intermittent-operating escalators and unawareness of building owners of potential energy savings constitute the main obstacles for the penetration of energy-efficient escalator technology. Even though intermittent operation provides large energy savings in low passenger traffic, little research has been devoted to analyzing the composition of total energy consumption in these situations. This paper presents observations and data gathered during long-term energy measurements of an intermittent-operating escalator pair (one upwards, one downwards operating), equipped with reduced speed and stop & go modes, installed in a store in the Helsinki area. Energy consumption measurements were performed concurrently with people counting. The effect of passengers walking on the step band on daily energy consumption was calculated by relying on the people counting sensor data. Additional experiments were conducted by applying different masses to analyze the dependence of energy consumption on the carried load. Finally, the effect of intermittent operation, or speed control with stop & go functionality, on energy consumption was calculated for the escalator pair on the given site. The energy consumption is reduced up to 42% in the upwards running escalator and 52% in the downwards running escalator compared to a continuously running escalator with the same operating hours. The study concludes that the power consumption profiles of the intermittent-operating escalator pair highly depend on the characteristics of the people flow, mainly the consistency of the people flow. [ABSTRACT FROM AUTHOR]

This paper presents a detailed investigation and analysis of the energy consumption characteristics of three institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. Identification models are developed to predict the real daily energy consumption data. Developed models involve three specific functions to represent the variability of the daily occupancy, the additional occupancy due to visitors and the variation of outdoor air temperature. The performance of the developed identification model is very satisfactory and fits very well with the real energy consumption data. Based on the identification model, key factors which are influencing the energy consumption in institutional buildings are identified as the variability of the daily occupancy, hence a methodology is developed to calculate the occupancy in the building. The identified parameters are used as inputs into deterministic energy simulation programs, like Energy Plus, to perform detailed energy analysis. The whole methodology developed has improved significantly the accuracy of the energy simulation modeling of institutional buildings and has permitted to understand and evaluate the major energy characteristics of these buildings. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology that provides a tool to support decisions in the monitoring of electrical energy consumption. The purpose is to help the manager of a set of non-residential buildings, such as, for example, factories, hospitals or university buildings, to objectively compare the energetic performance of all the installations under its responsibility and take measures when necessary. Firstly, an energy consumption model is developed considering the variables that have an influence on energy consumption in the building and processing them using the appropriate multivariate statistical techniques. Then, an efficiency index is defined which allows the manager to monitor the evolution of the installations and detecting deviations that later can be investigated, from an energetic efficiency point of view. A case study is presented where the developed methodology is applied to the public hospitals in the region of Castilla y León (Spain). [ABSTRACT FROM AUTHOR]

• A top-down analysis of the non-residential building stock in Brazil is proposed. • The share of each building typology in the country energy consumption was assessed. • A statistical analysis showed variation of EUI according to type of building and region. • Reference values for the commercial building stock in Brazil is provided. This paper provides a pioneering, top-down analysis of the non-residential building stock in Brazil. A country-wide dataset was used, comprising 10,000 buildings divided into 12 typologies. Key indicators include floor area, energy use intensity, and operation patterns. In addition, predominant wall constructions, lighting systems, equipment, water heating, and HVAC systems for each typology were identified, including average density power for the systems. Data from the Brazilian Energy Balance report was correlated with the sample of buildings to determine the proportion of each building typology in the total energy consumption of the country, considering different energy sources. This study provides an innovative and up-to-date status of the characteristics of non-residential buildings in Brazil. A complementary statistical analysis addressed the correlation between energy use intensity and the building characteristics including an ANOVA test and a regression analysis in order to investigate the influence of the energy usage indicators (Lighting Power Density, Equipment Power Density, Cooling capacity) on the energy use intensity of each building typology in Brazil. This study is intended to serve as a reference for building characteristics and building energy performance in Brazil, and hence support diverse research regarding country-level building stock modeling, energy benchmarking, climate change impacts, and energy-efficiency related studies. [ABSTRACT FROM AUTHOR]

• A new methodology to investigate the impact of thermal mass is defined. • The use of unrepresentative boundary conditions could lead to inaccurate conclusions. • High thermal mass helps to decrease and regulate the internal temperature. • High thermal mass could increase energy use in the summer and winter seasons. • Isolating thermal mass through internal insulation results in better energy performance than external insulation. Contradictory conclusions have been reported on whether thermal mass reduces or increases energy demand in hot and cold climates. A novel approach to measuring the impact of thermal mass on the building's energy consumption is presented. The method aims to better represent reality and applies two different control conditions simultaneously in a numerical model: the first condition, which has been used in past studies, adjusts the internal boundary condition based on the time duration of heating/cooling activation (simulating an occupancy pattern) and the second, which has not been used in past studies, adjusts the internal boundary condition based on both the time duration and the internal temperature (simulating an occupancy pattern and the thermostatic control system). When investigating the impact of thermal mass on the internal temperature in a scenario where no heat is transferred into the building, the high thermal mass outperforms the low thermal mass, showing a time-shifting effect, long time lag, and small decrement factor. However, when modelled using the method presented in this paper, high thermal mass results in greater energy consumption during summer and winter periods in Saudi Arabia. During transition seasons where temperature swings occur on either side of the indoor set-point temperatures, thermal mass reduces thermal transmission. The impact of thermal mass is also investigated for several climatic conditions in Saudi Arabia. Simulation results show that an internally exposed thermal massive wall section results in greater energy consumption when compared to a similar building with isolated thermal mass (i.e. insulated internally). This is due to the fact that even though the internal temperature inside the building is being cooled/warmed up to the set-point, the thermal mass continues to transfer heat that is absorbed by the building's fabric from/to the interior, placing a greater demand on the cooling/heating system. Over the course of a year, however, the impact of thermal mass is negligible when compared to other factors, such as the insulative properties of the building's fabric. [ABSTRACT FROM AUTHOR]

• Meter-level anomaly detection and diagnosis is performed through CMP algorithm. • CMP parameters are automatically set using supervised and unsupervised methods. • A severity score, based on 4 statistical methods, is used to recognize anomalies. • 55 anomalous energy consumption patterns are discovered in a one-year timeseries. • Anomalies at meter-level are diagnosed by exploiting information at sub-load level. Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads. [ABSTRACT FROM AUTHOR]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

• A kNN-BS method is proposed to repair lighting and plug energy data. • Using cross-validation method to select the optimal k. • Using 2-step data classification method to decide day type. • Achieved higher accuracy than traditional k NN. The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the k NN algorithm, this paper proposes a k NN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the k NN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of k NN-BS is less than 4% for working days and less than 6% for non-working days. Compared with k NN, the average CVRMSE of the k NN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The k NN-BS algorithm shows excellent stability and robustness compared with k NN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The k NN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs. [ABSTRACT FROM AUTHOR]

The criteria used conventionally in selection of structural system characteristics include mainly performance related and economic criteria and the environmental impacts including life cycle carbon footprint are rarely taken into consideration. This paper highlights the importance of considering the life cycle carbon footprint in selection of the structural system for buildings by illustrating the significant effect that variations in the structural system characteristics including type of the lateral load resisting system, material and height of the structure may have on the carbon footprint of structures. A set of 15 alternative steel and concrete structural systems including moment resisting frames, braced frames, shear wall systems and dual systems are designed for 3, 10 and 15 storey buildings. The carbon footprint of each individual design is estimated by considering the emissions incurred in material extraction, transportation, construction, operation and end-of-life phases using a computational method. The results indicate considerable differences between the life cycle carbon of different structural systems and between the carbon emissions incurred in different life cycle phases of a particular structural system, confirming the importance of considering the life cycle carbon footprint in selection of structural system characteristics. [ABSTRACT FROM AUTHOR]

The Swedish energy performance certificates for single-family housing provide rich information on energy consumption and various physical attributes. They also include estimates of the energy conservation potentials resulting from implementing cost-efficient energy saving measures. By matching the certificates issued for single-family houses in 2009 and 2010, with socio-economic data about the residents, local climate data and information about recent improvements of the building we have created a unique database, which can be used to explore a wide variety of questions related to energy consumption and conservation. One aim of this paper is to assess the role for energy consumption played by socio-economic characteristics of the residents as compared to physical attributes of the house. Another is to estimate the influence of housing attributes and climate on the “engineering estimates” of the conservation potentials. Our results show that while the quantitative impact of physical attributes dominate the energy use for heating and cooling, the opposite holds for household electricity. The assessed conservation potential, amounting to 15% of the energy consumption, is significantly related to both the housing attributes and the use of energy. The results also indicate a need to improve the information provided by the performance certificates. [ABSTRACT FROM AUTHOR]

Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Supermarkets are energy intensive. To optimize their energy consumption, major subcomponents need to be identified through disaggregation. Disaggregation can be challenging in supermarkets due to lack of instrumentation at sufficient resolution, especially in developing economies. Also, the diversity in the space cooling systems across stores adds to the complexity. This paper presents a novel approach to disaggregate store level energy into weather-dependent and weather-independent components using facility energy models. The novelty lies in the fact that the approach does not require sensory minutiae or extensive sub-metering. The approach identifies the key drivers of a supermarket's energy consumption, and also estimates gaps in the performance with possible root causes. A case study on using the proposed approach to analyze 94 stores from a supermarket chain is presented. Key findings include: (i) weather-independent loads can dominate weather-dependent loads. (ii) Refrigeration cases contribute to space cooling too. Cooling requirement of these cases can be more than that of space cooling. (iii) For these reasons, occupancy may not appreciably influence a store's energy consumption. (iv) In the supermarket chain studied, various reasons for poor performance spanning both design and operations were discovered, viz., higher deadloads, oversized HVAC systems, and additional load turn-on hours. [ABSTRACT FROM AUTHOR]

Several conflicting criteria exist in building design optimization, especially energy consumption and indoor environment thermal performance. This paper presents a novel multi-objective optimization model that can assist designers in green building design. The Pareto solution was used to obtain a set of optimal solutions for building design optimization, and uses an improved multi-objective genetic algorithm (NSGA-II) as a theoretical basis for building design multi-objective optimization model. Based on the simulation data on energy consumption and indoor thermal comfort, the study also used a simulation-based improved back-propagation (BP) network which is optimized by a genetic algorithm (GA) to characterize building behavior, and then establishes a GA–BP network model for rapidly predicting the energy consumption and indoor thermal comfort status of residential buildings; Third, the building design multi-objective optimization model was established by using the GA–BP network as a fitness function of the multi-objective Genetic Algorithm (NSGA-II); Finally, a case study is presented with the aid of the multi-objective approach in which dozens of potential designs are revealed for a typical building design in China, with a wide range of trade-offs between thermal comfort and energy consumption. [ABSTRACT FROM AUTHOR]

Energy consumption has become an increasingly controversial issue in the modern world. Among the widest range of energy consumers, residential buildings consume the largest amount of energy most of which is consumed by air conditioning systems in tropical countries. This paper attempted to examine energy saving in building elements such as walls, floors, windows, roofs, and ceilings and how the integration of such optimized elements in conjunction with effective air quality factor can contribute towards an ultimate energy efficient design. A typical two-storey terraced house in Kuala Lumpur, Malaysia was chosen to model energy usage by means of dynamic building Simulation. A case study was modeled using Revit Architecture software and analyzed using energy analysis software. Current energy consumption patterns were identified and the optimal level of energy usage was determined by replacing components with new energy efficient materials. Afterward, a Design of Experiment (DOE) method was used and the best combination of factor was identified. The results indicated that in residential buildings in tropical regions, changing ceilings and ceiling materials are the most effective way to reduce energy consumption; moreover, wall materials and inside temperatures were in the next levels of significant factors respectively. These results can be used to help building designers achieve optimum cooling load savings. [ABSTRACT FROM AUTHOR]

China has been the world's largest producer and consumer of air conditioners, and more and more RACs 1 1 The RACs is short for room air conditioners. would be owned by China's households along with the rapid economic development. Air Conditioner is also considered as one of the largest potential contributors to energy reduction among home appliances because of the huge energy consumption. Therefore, the national energy efficiency standards were issued to promote the use and production of high-efficient RACs. According to China's energy efficiency standards, this paper investigated the electricity savings and CO 2 emission reductions from RACs over the period of 2005–2025. The results indicate that the rural RAC market which develops more slowly than the urban one still has great potential, and government has to revise subsidy policies to make the standards more effective, especially for rural areas. In 2025, the total electricity consumption of RACs is projected to be 598–674 TW h, while the amount without energy efficiency standards is 753 TW h. From 2005 to 2025, the energy efficiency standards for RACs can save 1430–2540 TW h electricity and reduce 908.3–1610. 1 Mt CO 2 emissions in different scenarios. Finally, we suggest that the standards should be revised every 4 or 5 years with higher revision pace of 8% to 10% [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]

Radiant panels are known to be energy efficient sensible heating systems and a suitable fit for low-energy buildings. The main goal of this paper is to optimize the thermal insulation layers of house heated by using the radiant heating systems. This work is conducted to study four radiant systems: floor-heating system, wall-heating system, ceiling-heating system and newly developed floor-ceiling heating system located in a net-zero-energy house in Kragujevac, Serbia. The operation of these panels is simulated by software EnergyPlus. For the optimization of the optimal length of the thermal insulation software GenOpt is used. [ABSTRACT FROM AUTHOR]

Underfloor air distribution (UFAD) system, which is a relative new method of providing conditioned air and ventilation to the commercial buildings and data centers, is focused by researchers and designers for its potential advantages of better indoor thermal comfort (ITC) and indoor air quality (IAQ), layout flexibility, reduced life cycle costs, and energy saving. However, the application of UFAD system are still obstructed by the gap in the fundamental understanding of some new elements, such as the cooling load calculation, control of the thermal stratification, and evaluation of energy consumption. This paper reviews the current situation of UFAD technology, and the existing problems in recent researches are summarized. To improve the performance of UFAD system, several possible directions for the further studies are suggested. [ABSTRACT FROM AUTHOR]

• The outdoor environment indexes are integrated by simulation and empirical method. • Quantitatively study the interaction between green space and water bodies. • The proper layout of landscape planning can reduce the building carbon emissions. The rapid urban construction has made urban residential buildings become the main source of greenhouse gas emissions. So far, the existing studies on the impact of urban micro-climate on building energy carbon emissions have shown that green space (GS) and water bodies (WB) play positive roles in regulating outdoor thermal environment and reducing building energy consumption. Accordingly, this paper quantitatively explores the influence of the interaction between GS and WB on reducing building carbon emissions. First, the building's micro-climate is simulated by Computational Fluid Dynamics (CFD) to obtain outdoor thermal environment indexes, which are used as inputting data to calculate carbon emissions by software HDY-SMAD. Then, we utilize Geographic Information System (GIS) and Remote Sensing (RS) techniques to explore the natural surroundings of buildings, such as GS and WB distribution within the administrative scope of Dujiangyan. Next, the linear regression method is applied to analyze the relationship between GS, WB, and residential building carbon emissions, in which the residential carbon emissions data is obtained by social survey. Finally, the simulation results are verified by empirical research method. The findings demonstrate that the GS and WB have area, accessibility and seasonal differences in regulating local thermal environment and reducing building carbon emissions, and the overall regulation ability of WB for micro-climate is more significant than that of GS. In addition, the ratio area between GS and WB has the most significant influence on reducing building carbon emissions when it is 3:1 in winter, but this optimal ratio is 1:2 in summer. These conclusions can provide benchmark information for landscape planning and building energy conservation in the future. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal potential estimation of water source and their energy systems are reviewed. • Practical potential mainly depends on the geographical and temporal potential. • Pumping-well system requires high installation costs but can use high temperature. • Critical parameters for improving performance of entire system were investigated. In recent years, the utilization of a water source heat pump system (WSHP) has been proposed in South Korea as a solution to reduce energy consumption of buildings. To practically utilize the thermal energy of a water source, it is necessary to estimate the thermal potential of water sources and apply suitable energy conversion systems. However, there are extremely limited studies that conduct in-depth analyses to estimate the thermal potential of water sources and the energy conversion systems of water source heat pumps. In this context, this paper focuses on the review of the main issues in thermal potential estimation and energy conversion systems of water source heat pump systems. The estimation of thermal potential was reviewed in terms of theoretical, geographical, environmental, technical, and temporal potentials. The components constituting the system were investigated by dividing into heat pumps, water intake systems, and air handling units. In addition, the critical parameters for improving the performance of the entire system, such as building type, water source, and water intake distance, were investigated. It is expected that this study will be a valuable supplement to enable the thermal energy utilization of water sources to meet the growing energy demands. [ABSTRACT FROM AUTHOR]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

• Review identified 29 unintended impacts post residential energy retrofits. • Moisture and dampness are common issues reported in post retrofitted housing. • The indoor environment is significantly affected by airtightness and over-insulation. • Pre retrofit assessment is essential to avoid hygrothermal failure. Improving energy efficiency of existing buildings is currently among the most diverse and extensive mitigation opportunities to reduce energy consumption and CO 2 emissions worldwide. However, the implementation of energy-saving measures has caused unintended impacts, often correlated with dampness and mold growth connected to poor hygrothermal behavior in residential buildings. The focus of this paper is research on the impacts of energy efficiency measures (EEMs) in regard to the hygrothermal behavior resulting from the interaction of building's envelope, indoor environment, and occupants. The results show that dampness and mold growth are by no means exclusive to neglected houses, since the occurrence of these pathologies actually depends upon a complex set of conditions, including indoor and outdoor conditions, occupancy, maintenance, ventilation, mechanical systems, and quality of the envelope. We found that building envelope post-retrofit may suffer from higher levels of moisture and dampness, higher condensations risks, and a faster structural degradation caused by higher humidity levels. We also found that measuring hygrothermal behavior may play a role in more accurately predicting both overall energy consumption and occupant comfort. While hygrothermal behavior may be problematic, we found evidence that retrofits may moderately improve thermal comfort. [ABSTRACT FROM AUTHOR]

Abstract: One of the primary reasons for the application of cool materials is their energy and associated environmental impact on the built environment. Cool materials are usually applied on the roof of buildings to reduce cooling energy demand. The relative benefits of this reduction depend on the construction of the building, external weather conditions and use of the building. This paper examines the impact from the application of a reflective paint on a flat roof in a naturally ventilated office building in the area of London, UK where the climate is moderate with high heating demand by buildings. The environmental conditions (internal/external air and surface temperatures) of the building were monitored before and after the application of the cool roof during the summer. It was found that internal temperatures were reduced after the application of the cool roof. The building was modelled using TRNSYS and the model was calibrated successfully using the measurements. A parametric analysis was carried out by varying the reflectivity and insulation of the roof and ventilation rate; the heating and cooling demand for a year was calculated using the Summer Design Year for London as the weather file. It was found that cooling demand is significantly reduced, heating demand is increased and the total energy savings vary between 1 and 8.5% relative to an albedo of 0.1 for the same conditions. In free floating (naturally ventilated) buildings summer comfort is improved but there is a penalty of increased heating energy during the winter. Thermal comfort can be improved by an average of 2.5°C (operative temperature difference for a change of 0.5 in albedo) but heating demand could be increased by 10% for a ventilation rate of 2 air changes per hour. The results indicate that in the case of temperate climates the type, operation and thermal characteristics of the building should be considered carefully to determine potential benefits of the application of cool roof technology. For the examined case-study, it was found that a roof reflectivity of 0.6–0.7 is the optimum value to achieve energy savings in a cooled office, improve summer internal thermal conditions in a non-cooled office (albeit with some heating energy penalty). It indicates that it is a suitable strategy for refurbishment of existing offices to improve energy efficiency or internal environmental conditions in the summer and should be considered in the design of new offices together with other passive energy efficient strategies. [Copyright &y& Elsevier]

Data quality assurance of building energy consumption monitoring platform plays an important role in building energy consumption management. However, Data collected and transferred to the cloud platform is affected by many factors and shows some missing values and outliers in the time series. Combining with the correlation of smart meters on branches and regularity of building energy consumption characteristics, this paper proposes a synchronous prediction method for predicting building energy consumption in the secondary branch. In this model, synchronous data feature similarity (SDFS) model is used to find a similar energy consumption feature, Extreme gradient boosting (XGBoost) model is used to train and produce accurate prediction results which are compared with back propagation neural network (BPNN) and adaptive boosting (AdaBoost) and maximum distance outlier correction (MDOC) model can further correct the prediction results. Taking the daily energy consumption of the primary branch with a smart meter in the building energy consumption monitoring platform as the test object, the predition results of VRF energy consumption show that the MAE, MAPE, RMSE, and CVRMSE are 1.150, 0.142, 1.511, and 0.132 respectively, which is much lower than BPNN and AdaBoost. This study explores a novel feature mining method for historical data and an integrated model for outlier recognition and correction which significantly improves the accuracy of prediction results. Moreover, after correlation verification, the prediction model can be widely applied in building distribution system with sub-meter system which improves the data utilization rate of building energy management system. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

[Display omitted] • Review on application of PCM in trombe wall was performed. • Incorporation of PCMs results in minimizing energy consumption of buildings. • The optimum PCM location into buildings depends on the climate. • Immersion and direct incorporation causes leakage due to heat cycles. • The encapsulation techniques have successfully overcome all of drawbacks. Energy crises and environmental pollution are two of the cardinal obstacles that affect human life today. As part of the efforts to address the excessive energy demands in the last few decades is using renewable energy like solar. As the building sector continues to grow with the growing population, correspondingly, the demand for energy consumption required to heat and cool buildings for thermal comfort augments. To tackle this problem, techniques such as designing green buildings for sustainable architecture have been employed. One of these technologies is the Trombe wall which has been utilized as an avenue for heating buildings using renewable solar energy. Meanwhile, the practice of latent heat storage systems with phase change materials (PCM) is an efficient method to store energy. Substantial research has been carried out to assess the contribution of incorporation of latent heat storage and Trombe walls in buildings on their thermal energy performance. The present review paper communicates a summary of the recent advancements in this topic. The various designs of Trombe walls and different phase change materials types are presented. Research concerning the incorporation of PCM incorporation techniques is scrutinized. Finally, hitherto published literature regarding the applications of PCM in the Trombe wall is underscored and discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of the COVID-19 lockdown on energy consumption in university buildings was assessed. • Eighty-three academic buildings of the Universitat Politècnica de Catalunya - Barcelona Tech (UPC) were analysed. • Avoided energy consumption amounted to over 4.3 GWh in one year. • Vacant academic buildings still used 46.9% of their typical energy consumption. • Libraries (−74.2%) and teaching buildings (−68.1%) experienced the highest energy reductions during the strict lockdown. Exceptional pandemic lockdown measures enabled singular experiments such as analysing the energy consumption of vacant buildings. This paper assesses the impact of the COVID-19 lockdown on the energy use of academic buildings. For this purpose, weather-adjusted energy use was compared before and during the lockdown, including different levels of lockdown restrictions. Results obtained for the 83 academic buildings of Universitat Politècnica de Catalunya - Barcelona Tech (UPC) reveal that the avoided energy consumption amounted to over 4.3 GWh during the post-pandemic year. However, the results indicate that academic buildings were still using approximately 46.9% of their typical energy consumption during strict lockdown. This revelation emphasizes the high environmental burden of buildings, regardless of whether they are occupied. [ABSTRACT FROM AUTHOR]

[Display omitted] • Most of the reviewed studies (62%) focused on group-based comfort models. • ML models, especially PCMs outperformed conventional models (PMV and adaptive). • ML-based controls can improve IAQ while reducing energy consumption and CO 2 levels. • The most frequently used algorithms were SVM, ANN and Ensemble Learning. This paper provides a systematic review on the application of Machine Learning (ML) in thermal comfort studies to highlight the latest methods and findings and provide an agenda for future studies. Reviewed studies were investigated to highlight ML applications, parameters, methods, performance and challenges. The results show that 62% of reviewed studies focused on developing group-based comfort models, while 35% focused on personal comfort models (PCMs) which account for individual differences and present high prediction accuracy. ML models could outperform PMV and adaptive models with up to 35.9% and 31% higher accuracy and PCMs could outperform PMV models with up to 74% higher accuracy. Applying ML-based control schemas reduced thermal comfort-related energy consumption in buildings up to 58.5%, while improving indoor quality up to 90% and reducing CO 2 levels up to 24%. Using physiological parameters improved the prediction accuracy of PCMs up to 97%. Future studies are recommended to further investigate PCMs, determine the optimum sample size and consider both fitting and error metrics for model evaluation. This study introduces data collection, thermal comfort indices, time scale, sample size, feature selection, model selection, and real world application as the remaining challenges in the application of ML in thermal comfort studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classifications and strategies of NZEBs were reviewed. • Multi-criteria analysis supports NZEB designing. • Renewable energy supply options for NZEBs are summarized. • Cost-benefit analysis and life cycle assessment needed for criterion evaluation. Decarbonizing the building sector is extremely important to mitigating climate change as the sector contributes 40% of the overall energy consumption and 36% of the total greenhouse gas emissions in the world. Net-zero energy buildings are one of the promising decarbonization attempts due to their potential of decreasing the use of energy and increasing the total share of renewable energy. To achieve a net-zero energy building, it is necessary to decrease the energy demand by applying efficiency enhancement measures and using renewable energy sources. Net-zero energy buildings can be classified into four models (Net-Zero Site Energy buildings, Net-Zero Emissions buildings, Net-Zero Source Energy buildings, and Net-Zero Cost Energy buildings). A variety of technical, financial, and environmental factors should be considered during the decision-making process of net-zero energy building development, justifying the use of multi-criteria decision analysis methods for the design of net-zero energy buildings. This paper also discussed the contributions of renewable energy generation (hydropower, wind energy, solar, heat pumps, and bioenergy) to the development of net-zero energy buildings and reviewed its role in tackling the decarbonization challenge. Cost-benefit analysis and life cycle assessment of net-zero energy building designs and their challenges were reviewed to shape the priorities of future development. It is important to develop a universal decision instrument for optimum design and operation of net-zero energy buildings. [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the interactions of users with air conditioning systems, lighting, natural ventilation, internal shading devices, and costs related to automation of a teaching building. Three behavior patterns (proactive, intermediate and careless) were created. The proactive user seeks to use natural light, uses passive solar shading and integration of natural and artificial ventilation. The intermediate user does not seek integration of natural and artificial lighting, they use hybrid ventilation as a ventilation strategy. The careless user does not use any passive strategy, predominantly uses artificial conditioning and adjusts cooling and heating setpoint to values out of the norms. In addition to the users' behaviors, the automated building was modeled. Computer simulations were performed in the EnergyPlus program, in order to measure the influence of behavior patterns on the building's energy consumption. Based on the analysis of the building's energy consumption profile, results indicate users are between the intermediate and the careless user classification, with a tendency to the intermediate user profile. The paper also presents an analysis of the payback time with the building automation in face of the user profiles. [ABSTRACT FROM AUTHOR]

Abstract: Chiller sequencing control plays an important role in achieving air-conditioning system energy efficiency since chillers usually account for major part of system overall energy consumption in commercial buildings. This paper compares and evaluates the in situ performances of three commonly used chiller sequencing control strategies. These three control strategies are operating online in parallel on a management and communication platform in a super high-rise building in Hong Kong. The comparison and evaluation focus on the perspectives of chiller switch number, electrical peak demand and overall energy consumption in one week. Meanwhile, the different impacts of two systematic errors (i.e. positive errors which cause direct measurement larger than true value and negative errors which cause measurement smaller than true value) on each control strategy are also analyzed. The performance comparison demonstrates that the strategy, using fused cooling load measurement, achieved the best performance. The applications of the online computed chiller maximum cooling capacity and the advanced data fusion technique, which removes the negative impacts of sensor measurement uncertainties, can significantly improve the performance of chiller sequencing control. [Copyright &y& Elsevier]

Abstract: The commercial building sector has become the focus of many governmental energy reduction initiatives to achieve more sustainable development. Reducing building energy use starts by improving the design of buildings. To this end, energy modeling and simulation tools are used during the design phase to predict energy use and help designers choose and size the different building systems. Large discrepancies are however being observed between predicted and actual building energy performances. In order to determine the sources of errors and improve these predictions, the sensitivity of energy models to different input parameters needs to be evaluated. Studies in literature have extensively evaluated the sensitivity of models to the buildings’ technical design parameters. However, none considered the parameters related to the energy consumption behavior of occupants, leaving their impact on energy modeling unknown. This paper presents a comprehensive sensitivity analysis study performed on the occupancy behavioral parameters of typical office buildings of different size and in different weather zones. Significant sensitivity levels were observed, varying according to both building size and weather conditions. The highest sensitivity was obtained when varying the ‘heating temperature set point’ parameter in small-size buildings located in US weather zone 2 Dry. [Copyright &y& Elsevier]

Abstract: Energy certification is a tool that is increasingly relevant today, not only because it allows quantifying and monitoring energy consumption in the building sector but also because it is a booster mechanism of the implementation of international guidelines on energy consumption. The thermal characteristics are the base of the energy efficiency calculation. In this paper, some reference values related to St. António, the most populated parish of Funchal (Madeira), were quantified and analyzed demonstrating its moderate environmental conditions when compared with the rest of Portugal. The variation on the energy certification in last three years was studied for Portugal, Madeira, Funchal and St. António. In general the number of certificates has been decreasing, due to the crisis in the construction sector. All the Regulatory Declarations of Conformity (DCR) and the Energy and Indoor Air Quality Certificates (CE) emitted so far in the St. Antonio parish were considered to study the influence of some factors in the building final energy classification. The findings show that the year of emission, the orientation of the building, the type of building and the type of certificate affect the final energy label of the building. [Copyright &y& Elsevier]

Abstract: The current energy distribution infrastructure in many urban areas either cannot support anticipated future energy use or would require significant rehabilitation even if current use were maintained. Understanding the dynamics of local energy use is an important precondition of understanding how to remedy this situation. This paper builds a model to estimate the building sector energy end-use intensity (kwh/m2 floor area) for space heating, domestic hot water, electricity for space cooling and electricity for non-space cooling applications in New York City. The model assumes that such end use is primarily dependent on building function, whether residential, educational or office for example, and not on construction type or the age of the building. The modeled intensities are calibrated using ZIP code level electricity and fuel use data reported by the New York City Mayor''s Office of Long-Term Planning and Sustainability. The end-use ratios were derived from the Residential and Commercial Building Energy Consumption Survey''s Public Use Microdata. The results provide the ability to estimate the end-use energy consumption of each tax lot in New York City. The resulting spatially explicit energy consumption can be a valuable tool for determining cost-effectiveness and policies for implementing energy efficiency and renewable energy programs. [Copyright &y& Elsevier]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

• The degree of influence of residential cluster layout factors on building heating energy consumption levels in cold regions of China. • Provides an idea of residential layout optimization and design based on the energy saving objectives of residential clusters, translating quantitative indicators such as wind environment into residential layout form design problems. • Energy simulation-based residential planning and layout design method is used to quantify the relationship between residential cluster design factors and energy efficiency in Jinan, resulting in optimal design parameters for residential planning factors in Jinan. • The study of building heating energy consumption in a city in a cold region of China, with a methodology that can be used in countries or cities with similar climate characteristics. Residential heating energy consumption in China accounts for about 34% of the total building operation energy consumption in the country. The layout pattern of residential clusters is one of the most critical factors affecting building heating energy consumption, yet few scholars have studied the layout of residential clusters in a quantitative way. This paper takes Jinan, a typical city in cold region IIB of China, as an example, to quantify and analyse the residential layout parameters related to heating energy consumption: group layout, building orientation and building spacing; ECOTECT and HTB2 are used to model and simulate the energy consumption of the three types of layout residential groups selected for the study, and the relationship between residential group layout parameters and residential heating energy consumption is analysed based on multiple linear regression analysis. The relationship between residential group layout parameters and residential heating energy consumption was analysed based on multiple linear regression analysis. The study shows that, on the premise of ensuring the same building density, the group layout has the greatest influence on the building energy consumption, and the courtyard layout has lower heating energy consumption compared to the row and staggered layout; on this basis, the influence of building orientation and building spacing on heating energy consumption is discussed. The study shows that increasing the spacing between buildings to meet the daylight spacing will reduce energy consumption by 4% and that a 15° shift to the west will reduce energy consumption by 5%. This result confirms the importance of cluster layout in terms of heating energy consumption and urban energy efficiency in cities in cold regions. [ABSTRACT FROM AUTHOR]

An effective and accurate building energy consumption prediction model is an important means to effectively use building management systems and improve energy efficiency. To cope with the development and changes in digital data, data-driven models, especially deep learning models, have been applied for the prediction of energy consumption and have achieved good accuracy. However, as a deep learning model that can process high-dimensional data, the model often lacks interpretability, which limits the further application and promotion of the model. This paper proposes three interpretable encoder and decoder models based on long short-term memory (LSTM) and self-attention. Attention based on hidden layer states and feature-based attention improves the interpretability of the deep learning models. A case study of one office building is discussed to demonstrate the proposed method and models. Firstly, the addition in future real weather information yields only a 0.54% improvement in the MAPE. The visualization of the model attention weights improves the interpretability of the model at the hidden state level and feature level. For the hidden state of different time steps, the LSTM network will focus on the hidden state of the last time step because it contains more information. The Transformer model gives almost equal attention weight to each day in the coding sequence. For the interpretable results at the feature level, daily max temperature, mean temperature, min temperature, and dew point temperature are the four most important features. The four characteristics of pressure, wind speed-related features, and holidays have the lowest average weights. [ABSTRACT FROM AUTHOR]

• Two-year monitoring campaign of IEQ parameters of a nearly zero energy building was firstly presented in China. • The nearly zero energy building can provide the indoor with a comfortable thermal environment and good air quality with less energy demand. • The overheating problem in the whole year would be especially paid attention during design and operation. • The occupant behavior has a significant impact on energy consumption and thermal comfort. The paper presents a two-year field measurement and assessment of a nearly zero-energy Building in cold zones of China. These parameters of indoor environment quality (IEQ) and electricity consumption for HVAC are recorded. According to the measurement data, the most of indoor temperature was controlled in the range of 23 °C ~ 26 °C and relative humidity (RH) mainly fluctuates within the range of 30% ~ 60%. The concentrations of carbon dioxide (CO 2) and particulate matter (PM 2.5) are lower than the relevant standard requirements in most of the time. The average electricity consumption for HVAC was 33.05kWh/(m2·a) and the heating and cooling demands could be reduced by 55.2% and 54.0%, respectively, compared with existing buildings. In general, the NZEB can provide indoor comfortable thermal environment and indoor air quality with less energy demand, but the overheating problem in summer and effect of occupant behavior should be especially paid attention during operation. Overall, the experience from the actual operation of NZEBs will help China to reach its goals of reducing carbon emissions and addressing climate change through realizing the energy-saving potential of NZEBs. [ABSTRACT FROM AUTHOR]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

• Optimizing the integration of renewable energy in existing buildings. • Minimizing required cost to satisfy specified reduction in building energy consumption. • Optimal compliance with EISA2007 energy requirements for federal buildings. • Novel methodology for efficient segmentation of complex buildings. • Complying with building functional requirements and constructability constraints. This paper presents the development of a novel optimization model for optimizing the use of RE measures in existing buildings to satisfy an owner-specified target reduction in energy consumption while minimizing the required building upgrade costs. The model performance was evaluated using a real-life case study of an educational building with a total area of 20,717 m2. The optimization model was used to identify an optimal combination of RE technologies that minimize the upgrade cost for this case study while satisfying its specified 80% energy reduction goal. The model minimized the building upgrade cost by making a number of optimal selections, such as locating the majority of solar RE technologies in building segments facing south due to their higher exposure to the sun radiations. The results of this analysis highlight the original contributions of the model in identifying an optimal set of RE measures that minimize the building upgrade cost while satisfying all owner-specified requirements, providing a detailed description of the optimal building integration plan of RE technologies, and complying with practical functional requirements, and constructability constraints. These capabilities are expected to support building owners and decision-makers in their efforts to integrate cost-effective RE measures in existing buildings. [ABSTRACT FROM AUTHOR]

[Display omitted] • The research investigates urban clusters of tall commercial buildings in Tallinn. • A workflow integrating thermal models of indoor and outdoor domains is developed. • Indoor comfort and energy consumption of tall commercial buildings are analyzed. • First-time assessment of outdoor thermal comfort in commercial areas in Tallinn. • The research suggests building layouts for improving indoor and outdoor comfort. Considering climate change, controlling outdoor microclimates is an increasingly pressing concern. Microclimates have a significant effect on both outdoor and indoor comfort, and on the energy efficiency of buildings. This concern is particularly important as current climate conditions reveal that warmer summers are threatening the comfort of pedestrians and causing overheating in office environments, which is consequently increasing cooling energy consumption. A further concern is that this trend now extends to Nordic latitudes. Existing literature demonstrates how a local microclimate depends on many factors such as urban density, shape and orientation of buildings, the types of materials present, the number of green areas and anthropogenic activities. However, there is little research focusing on how reciprocal distances among tall buildings, and their relative position, affect outdoor and indoor comfort, and the associated energy consumption of buildings. This paper presents a unique and comprehensive insight into the interconnected nature of indoor and outdoor comfort via coupled simulations. It presents a study of clusters of tall commercial buildings located in the Nordic climate of Tallinn (Estonia) with different microclimates, and shows that the differences are due to variable shadowing and reflections and different wind patterns. The results, which focus on summer conditions, show that small variations of cluster layout strongly affect the local indoor and outdoor comfort, thus highlighting the need to conduct both studies simultaneously in research aiming to increase pedestrian and indoor comfort and resource efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • Building energy consumption is largely driven by diversity in occupant behavior. • Machine learning can be used to predict lighting energy use with occupancy data. • Building layouts are optimized with a naïve clustering method and genetic algorithm. • Layout optimization can yield 5–6% reduction in lighting energy use. One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship, and optimizing the layouts of buildings. The optimization makes use of both a clustering-based approach and a genetic algorithm, and it aims to reduce energy consumption. We find in a case study that nonhomogeneous behavior (i.e., high diversity) among occupant schedules positively correlates with the energy consumption of a highly controllable lighting system. We additionally find through data-driven simulation that the naïve clustering-based optimization and the genetic algorithm (which makes use of the energy simulation engine) produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 151 occupants. Overall, this study demonstrates the merits of utilizing low-cost dynamic design of existing building layouts as a means to reduce energy usage. Our work provides an additional path to reach our sustainable energy goals in the built environment through new non-capital-intensive interventions. [ABSTRACT FROM AUTHOR]

• Characterizes the urban heat island of Leeds and quantifies the intensity at an hourly resolution. • Demonstrates the importance of site topography and altitude when using air temperature data to quantify urban heat island intensity. • Creation of urban building simulation weather files for comparison with those used for regulatory compliance from a rural site. • Identifies the link between building occupancy patterns and the diurnal nature of urban heat islands. Urban heat islands are evident throughout the world and may become more problematic in temperate climates as global warming continues. This paper characterizes the urban heat island of Leeds, a city in the temperate maritime climate of the UK. Measured weather data from rural and urban sites have been used to quantify the heat island and to create building simulation weather files for the summer of 2013. These weather files have been used to model the impact on energy consumption and thermal comfort in notional non-domestic and domestic buildings, including a hotel, office, house and apartment. The heat island intensity is found to be greatest at night, with the peak in this data set reaching 5.9 °C at 20:00 on 2nd August 2013, with an average peak value of 2.3 °C occurring at the same hour for the case study period. Daytime temperatures were however similar at urban and rural sites. In the notional hotel building, air conditioning costs were increased by up to 40% due to night-time loads whereas consumption in the notional office was similar in all locations. Air temperature, and especially night-time temperature, increased in the domestic examples by an average of approximately 95 additional hours above 25 °C. This work expands knowledge by characterizing the Leeds urban heat island, demonstrating the importance of rural reference site selection for city regions that span a range of altitude, quantifying the gap in building performance when using regional or city centre weather files, and linking building occupancy patterns to the diurnal profile of the urban heat island. [ABSTRACT FROM AUTHOR]